In this paper, a neurofuzzy adaptive control framework for discrete-time systems based on kernel smoothing regression is developed. Kernel regression is a nonparametric statistics technique used to determine a regression model where no model assumption has been done. Due to similarity with fuzzy systems, kernel smoothing is used to obtain knowledge about the structure of the fuzzy system and th...

In this work we develop an input-output recurrent neurofuzzy network in discretetime for identification and control of nonlinear systems. The structure is linear in the consequent parameters and nonlinear in the antecedent ones. The training of the antecedent parameters is achieved by linearizing them around a suboptimal value, assuming that the only known data are input-output signals obtained...

Marquees are temporary light structures that are connected to the ground by tensile anchors to resist forces imposed by wind acting on the structure. Failures of such structures are not rare and have resulted in deaths and tens of thousands of dollars of damage. Consequently, an accurate estimation of the ultimate pullout capacity of ground anchors is essential; however, current methods for est...

This paper investigates the nonlinear predictability of technical trading rules based on a recurrent neural network as well as a neurofuzzy model. The efficiency of the trading strategies was considered upon the prediction of the direction of the market in case of NASDAQ and NIKKEI returns. The sample extends over the period 2/8/1971–4/7/1998 while the sub-period 4/8/1998–2/5/2002 has been rese...

1.1 A Transputer Mobile Robotics System Mobile robots have received a considerable attention from early research community, from (A. Benmounah, 1991), (Maamri, 1991), (Meystel, 1991) up to this instant (Hegazy, et. al, 2004), and (Pennacchio, et. al., 2005). A fuzzy or neural control Transputer based control mobile robots has received, rather, little attention. A number of, are (Welgarz,1994), ...

A neural network can approximate a function, but it is impossible to interpret the result in terms of natural language. The fusion of neural networks and fuzzy logic in neurofuzzy models provide learning as well as readability. Control engineers find this useful, because the models can be interpreted and supplemented by process operators.

This paper reports on the design and implementation of a neurofuzzy system for modelling and controlling drilling processes in an Ethernet-based application. The neurofuzzy system in question is an Adaptive Network based Fuzzy Inference System (ANFIS), where fuzzy rules are obtained from input/output data. The design of the control system is based on the internal model control paradigm. The mai...

Modelling has become an invaluable tool in many areas of research, particularly in the control community where it is termed system identification. System identification is the process of identifying a model of an unknown process, for the purpose of predicting and/or gaining an insight into the behaviour of the process. Due to the inherent complexity of many real processes (i.e multivariate, non...